June 2016: Scripts of the Week

We saw a healthy mix of fantasy and reality in June's scripts of the week. Whether you're a huge World of Warcraft fan (or just nostalgic, like me) or you've been closely following the 2016 US Election, the scripts from last month feature great analyses that will appeal to broad tastes. Oh, and if you're looking for a way to get your Game of Thrones fix now that season 6 has ended, did you know you can analyze the characters here on Kaggle?

This month's scripts include some excellent storytelling, phenomenal presentation using Rmarkdown, and visual interactivity. Read on to learn about the following:

Why you should spend some time checking your assumptions about the data you're working with.

While Clinton was champion of the primaries, Sanders was king of the caucuses. But how was the vote share divided by dimensions like income and college attainment?

How to delve into the virtual lives of 91,065 WoW avatars using stunning interactive visualizations.

Where in the world have people been surfing the internet over time? (Or logging into WoW.)

What motivated you to create it?

I am a big fan of Game of Throne series, the moment I noticed GoT dataset in Kaggle I was really excited. Initial excitement led me to walk through all the analysis reports submitted to see what exactly this data is all about. They were all amazing reports, the report submitted by Shail Jayesh Deliwala was quite a piece of art. His report was so inspiring that made me download the dataset and go deeper. The moment I realized the data is of high quality and there are quite a few doing research on GoT books... I decided to do an analysis of my own.

What have you learned from the code/output?

Although I had watched all the episodes of GoT, I never had opportunity to read any of the books. I always had the doubt whether the book is transformed into TV series exactly or things were modified to create more excitement among the audience. Looks like the sanctity of the book is maintained in the TV series.
I also identified a mistake in the data. According to the data, “Battle of Castle Black” was faught and won by Mance Rayder. In reality that is not true, Stannis wins the battle and try to burn Mance Rayder alive. Later Stannis gets killed by Brienne of Tarth.

There is a big learning from the above mistake: It is not necessary that the data is always right. There may be human errors, wrong assumptions, etc. while crafting the dataset. It is very important that analysts/scientists involved in Data Science should spend significant amounts of time in assessing the correctness of the source data before venturing into bigger things.

What other questions would you love to see answered or explored in this dataset?

There are 2 things I would like to see answered...

The key thing about Game of Thrones is its unpredictable nature. If someone had researched and crafted a dataset of predictions on unpredictable entities, I would like to take the data and reverse engineer to find their algorithm.

In GoT whenever there an important death occurs, another character takes over the importance of the dead person. For example:

When Ned Stark died Robb Stark became an important person

When Robb Stark died Jon Snow became the person of importance

When Jofferey Baratheon died Tommen Baratheon took over

After Roose Bolton, Ramsay Bolton took his place

When Balon Greyjoy died Euron Greyjoy appeared to become a character of interest

My quest will be to predict who will die and who else will become a point of interest after the death.

In this script, Joshua analyzes the impacts of social cleavages on vote share for Clinton versus Sanders in the 2016 US election primaries. In his analysis (which is an outstanding exemplar for a well-presented analysis), Joshua considers how factors like race, income, college attainment and percentage of population below poverty divided vote share between the two presidential hopefuls.

What motivated you to create it?

I am a big fan of World of Warcraft since very young. I have played the game for about a year back when it was released and now I end up working as Data Scientist, curious about every tiny bit of the game, eager to find patterns, learn about player behaviors and answer questions I had for more than 10 years. So well, why not? Maybe I'm one of those char ids. 🙂

What have you learned from the code/output?

Doing the retention analysis I found some drops in specific ranges of days since install (x-axis). I wrote down some hypotheses for the drops and moved on. After doing the "Play Time" plot I found some days where we didn't have data (probably because the server was down) and that discovery completely changed the drops hypotheses. So make sure you always explore every corner of your data to make sure your hypotheses are as accurate as possible.

WoW play time heatmap.

What other questions would you love to see answered or explored in this dataset?

One of the most intriguing thing is the peak of new users at Oct 8th on "Created Characters Created Over Time" plot. Most of them (45%) Warriors. If you look to the average number of characters per Class, Warriors represent about 20.1%. So Oct 8th is way above the mean. I tried to find some events that occurred on that day but couldn't find much. The only guess so far is the patch 3.0.2. Any WoW dino around for some hints here? 🙂

What motivated you to create it?

As a part of my graduate qualification work I researched the internet's distribution around the globe. The “World Development Indicators” dataset seems to me very interesting due to large amount of various data, that’s why I chose it.

What have you learned from the code/output?

I've learnt how to work with data in R and visualize results. And now it's really easy for me to analyze the internet's spread from 1991 to 2014. I got useful experience while operating with rworldmap package. Finally I published my first script on Kaggle. There were some difficulties with its execution, but I quickly enough dealt with them.